Summary of Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference, by Matthew Riemer et al.
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
by Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the limitations of applying machine learning models in real-time systems, where reaction time is crucial. It shows that minimizing long-term regret is generally impossible within the typical sequential interaction and learning paradigm, but becomes possible when sufficient asynchronous compute is available. The authors propose novel algorithms for staggering asynchronous inference processes to ensure consistent action-taking intervals. They demonstrate that using models with high action inference times is only constrained by environment stochasticity over the inference horizon, not by action frequency. The number of inference processes needed scales linearly with increasing inference times, enabling use of larger-than-usual models when learning from real-time simulations like Pokémon and Tetris. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers investigate how machine learning works in situations where decisions need to be made quickly. They found that using big neural networks is often not possible because they take too long to make a decision. The team proposes new ways for computers to process information asynchronously, so actions can still be taken on time. This allows the use of bigger models, which are better at learning from real-time simulations like playing Pokémon or Tetris. |
Keywords
» Artificial intelligence » Inference » Machine learning
